AI Scientist / Senior AI Scientist

Near-term focus areas

• Predictive ordering: ML and AI capabilities that improve how customers plan and place orders.
• Agentic copilots for workflow management: intelligent assistance embedded in core product workflows (technical direction weighted toward Senior AI Scientist hires).

Shared responsibilities

• Identify high-leverage AI opportunities using business context, data diagnostics, and technical feasibility.
• Design practical AI/ML solutions (leveraging both deterministic and LLM/agent-based patterns where appropriate) with clear trade-offs on accuracy, latency, cost, and reliability.
• Build and productionize complex model systems with engineering-quality discipline: testing, observability, rollback/fallback strategy, human-in-the-loop integration, and incident readiness.
• Define evaluation frameworks that connect offline/online model quality to KPI impact and risk/accuracy controls.
• Partner closely with product, engineering, analytics, and operations to align scope, sequencing, and accountability.

AI Scientist responsibilities

• Drive hands-on delivery on predictive ordering capabilities from early production through optimization.
• Work closely with a principal-level data scientist on architecture choices while owning execution velocity.

Senior AI Scientist responsibilities

• Set technical direction for agentic workflow / copilot capabilities in partnership with product and engineering leadership.
• Co-own prioritization and standards with product, engineering, and data leadership — not execution alone.
• Mentor scientists and technical peers on applied AI execution, production quality, and pragmatic delivery.

Required qualifications

Baseline

• Proven track record delivering AI/ML systems to production with measurable business outcomes.
• Deep familiarity with current LLM and agent technologies, including practical evaluation and failure-mode handling.
• Demonstrated ability to productionize complex models and model-adjacent systems with strong reliability and observability practices.
• Heavy, day-to-day use of AI-native engineering workflows (coding, framing/design, debugging, and code review) for at least the past 18 months.
• Working implementation proficiency across at least two technical ecosystems/cloud stacks (for example AWS and GCP).
• Strong quantitative foundation in experimentation, statistical reasoning, and model evaluation.
• Strong collaboration skills; can drive alignment and decisions under ambiguity.

AI Scientist Level

• 5–7+ years in applied data science / machine learning roles with repeated production delivery.
• Track record owning initiatives end-to-end—not only contributing to models owned by others.
• Leadership-level influence within a cross-functional squad; improves team decision quality through technical rigor.

Senior AI Scientist Level

• 8+ years in applied data science / machine learning roles with portfolio-level outcome ownership.
• Track record owning AI/ML initiatives from concept through production and measurable business impact at cross-team scope.
• Stakeholder leadership across product, data, engineering, and operations; can resolve prioritization under ambiguity.

Preferred qualifications

Experience with agentic systems, LLM evaluation frameworks, production MLOps on cloud platforms, and B2B workflow automation.